Pioneering the next generation of AI requires breakthrough innovations in GPU performance and systems engineering. As a GPU Performance Engineer, you will architect and implement foundational systems that power Claude and push the frontiers of what is possible with large language models. You will maximize GPU utilization and performance at unprecedented scale, developing cutting‑edge optimizations that enable new model capabilities and dramatically improve inference efficiency. Working at the intersection of hardware and software, you will implement state‑of‑the‑art techniques from custom kernel development to distributed system architectures, spanning the entire stack—from low‑level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.
Performance Engineer, GPU at Anthropic
Hybrid - San Francisco, CA
More jobs at AnthropicSalary
USD 280,000 - 850,000
Requirements
Skills
- Deep experience with GPU programming and optimization at scale
- Impact-driven, passionate about delivering measurable performance breakthroughs
- Ability to navigate complex systems from hardware interfaces to high-level ML frameworks
- Enjoy collaborative problem-solving and pair programming
- Desire to work on state-of-the-art language models with real-world impact
- Concern for societal impacts of AI
- Ability to thrive in ambiguous environments where the path forward is defined
- Track record of delivering transformative GPU performance improvements in production ML systems
- Bachelor’s degree or equivalent combination of education, training, and/or experience
- Experience with GPU Kernel Development (CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization)
- Experience with ML Compilers & Frameworks (PyTorch/JAX internals, torch.compile, XLA, custom operators)
- Experience with Performance Engineering (kernel fusion, memory bandwidth optimization, Nsight profiling)
- Experience with Distributed Systems (NCCL, NVLink, collective communication, model parallelism)
- Experience with Low-Precision techniques (INT8/FP8 quantization, mixed-precision)
- Experience with Production Systems (large-scale training infrastructure, fault tolerance, cluster orchestration)
Responsibilities
- Architect and implement foundational systems that power Claude and enable large language models
- Maximize GPU utilization and performance at unprecedented scale
- Develop cutting-edge optimizations that directly enable new model capabilities and improve inference efficiency
- Implement state-of-the-art techniques from custom kernel development to distributed system architectures
- Span the entire stack from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization
- Co-design attention mechanisms and algorithms for next-generation hardware architectures
- Develop custom kernels for emerging quantization formats and mixed-precision techniques
- Design distributed communication strategies for multi-node GPU clusters
- Optimize end-to-end training and inference pipelines for frontier language models
- Build performance modeling frameworks to predict and optimize GPU utilization
- Implement kernel fusion strategies to minimize memory bandwidth bottlenecks
- Create resilient systems for planet-scale distributed training infrastructure
- Profile and eliminate performance bottlenecks in production serving infrastructure
- Partner with hardware vendors to influence future accelerator capabilities and software stacks
Technologies
CUDATritonCUTLASSFlash AttentionTensor core optimizationPyTorchJAXtorch.compileXLACustom operatorsNsightNCCLNVLinkINT8FP8Mixed-precision techniquesLarge-scale training infrastructureFault toleranceCluster orchestration
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